Fuzzy Cluster Analysis and Simplification

Abstract

The term cluster analysis does not identify a particular statistical method or model, as do discriminant analysis, factor analysis, and regression. You often don't have to make any assumptions about the underlying distribution of the data. Using cluster analysis, you can also form groups of related variables, similar to what you do in factor analysis. There are numerous ways you can sort cases into groups. The choice of a method depends on, among other things, the size of the data file. Methods commonly used for small data sets are impractical for data files with thousands of cases. In this master dissertation the fuzzy c-means algorithm will be analyzed and modified. The drawback of the algorithm is that it should be paid by the user number clusters. So optimal grouping is achieved by using the validity index according to M.Y. Chen and D.A. Linkens, which is discussed in Chapter 4. Chapter 5 presents a simulation for various data (FCM Data, Data of nonlinear functions). The algorithms have been implemented in the Matlab programming environment.